Engagement Metrics: Uses and Discoveries
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Transcript of Engagement Metrics: Uses and Discoveries
Reggie Bustinza
Director of Alumni Relations – Lewis University
Joe Volin
Asst. Dir. of Alumni Relations – Lewis University
Romeoville, IL
Private Catholic institution
6,500 students (4,500 undergrad, 2,000 grad)
40,000 alumni – primarily in Chicago area
Alumni Relations staff of 3
Database: Raiser’s Edge (“R/E”)
Why try to measure engagement?
The Lewis System◦ Process
The Value in Metrics
Results
Metrics can guide decision making◦ Spot trends
◦ Identify opportunities
◦ Abandon dead ends
◦ Quantify program success
More efficiency & efficacy
Justify our existence
Established in 2012
Created in-house
Created by Alumni Staff (Joe Volin and Reggie Bustinza)
Requirements◦ Work with existing data◦ Comprehensive◦ Searchable (integer data)◦ Valid as aggregate and/or individual data◦ Easy to understand
Not required, but nice to have…◦ Inexpensive to implement◦ Ability to run ourselves, as frequently as we want
OR dynamic◦ Option to exclude data to look for correlations◦ Simplicity
Process1. Make sure database is capable of outputting what
we desire, and we can import results
2. Identify what relevant data we track – “What information do we have that shows some kind of engagement?”
3. Assign relative values
4. Test
5. Repeat until values are no longer questioned
Step 1: Can Database Handle It?
Step 2: What do we Track?
Is it indicative of engagement?
Is the data consistent and accurate?
Will we keep tracking it?
• Event Attendance• Giving – how much
and how often• Valid email• Open emails• Social media• Valid address
• Valid business info• Board member• Award winner• Legacy parent• Campus visits• Interested volunteer• Active volunteer
Can we categorize?
EventsEvent Attendance
GivingGiving – how much and how often
CommunicationsValid emailOpen emailsSocial mediaValid addressValid business info
VolunteerismActive volunteerBoard memberInterested volunteer
OtherAward winnerLegacy parentCampus visitsEmployeeAffinity Partners
Strengths Weaknesses
Can run in house
Values recent activity over old activity
Results are easy to understand
Some data can be suspect (eg: acquired mailing lists)
Have to export, use two programs, then import for scores
As data points are added, historical scores are distorted
Challenges◦ Not all board members are equally engaged.
◦ How stratified should we make giving levels?
◦ Free events vs. Paid events
6 scores are actually produced◦ 1 for each category
◦ Overall Engagement Score (sum of each category)
Share values with colleagues for feedback
Run the numbers, see what results are
Spot check results
Pull top 10, top 20, top 50, top 100 alumni◦ Does it add up?
◦ If not, why?
Tweak values, repeat test
Run Quarterly (past 12 months)
Exported each category to Excel where values are assigned and coded
SPSS is used to merge data
Import integers back into Raiser’s Edge
Metrics are half of the battle. The real question is: How will you use this tool?
◦ Whittle mailing lists We have re-allocated more than $15,000 in printing costs in the 3 years
since we have had metrics.
◦ Identify prospects that were under-the-radar Identified 552 top engaged alumni with high wealth scores that had not been
identified through traditional prospect research Resulting in 186 portfolio assignments; and 45 initial visits during Fiscal Year
2015
◦ More efficient Annual Fund calling lists 118 new donors in the categories that utilized engagement metrics for
further segmentation (FY14 vs FY15)
◦ Identify potential affinity groups
◦ Evaluate programming
Advancement programming can create higher levels of engagement.
Higher levels of engagement lead to higher giving participation.
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Engagement Comparison
GivingScore Communications Events
Category Giving Events Comm. Vol. Other TOTAL Give %
FY15
All Alumni 1.329 0.184 9.531 0.095 0.235 11.375
Young Alumni
1.250 0.256 10.840 0.180 0.084 12.670
Athletes 2.942 0.684 10.572 0.138 0.528 15.279
Volunteers 6.84 2.94 15.08 5.33 0.96 31.26
Aviation 0.91 0.11 9.05 0.16 0.19 10.46
Nursing 0.96 0.06 8.83 0.06 0.24 10.45
Law & Justice
0.86 0.10 9.24 0.06 0.24 10.50
Top X % Point Cutoff
1% 48
5% 26
10% 19
25% 14
50% 9
75% 6
Top X % Point CutoffGiving
ParticipationGiving
Participation
1% 48+ 424/426 99.5%
5% 26-47 1491 / 1680 88.8%
10% 19-25 944 / 2811 33.6%
25% 14-18 468 / 5097 9.2%
50% 9-13 229 / 9940 2.3%
75% 6-8 1 / 14883 <0.001%
100% 0-5 0 / 4810 0.00%
Top X % Point CutoffGiving
ParticipationGiving
Participation
1% 28+ 397/397 100%
5% 12-27 1783/1783 100%
10% 2-11 1377 / 2218 62.1%
25%
50%
75%
100% 0-1 0 / 35249 0.00%
Top X % Point CutoffGiving
ParticipationGiving
Participation
1% 21+ 340 / 922 36.9%
5% 19-20 370 / 1335 27.7%
10% 17-18 500 / 2147 23.3%
25% 12-16 842 / 6657 12.6%
50% 8-11 1195 / 16882 7.1%
75% 4-7 305 / 10319 2.9%
100% 0-3 5 / 1385 0.36%
Top X % Point CutoffGiving
ParticipationGiving
Participation
1% 5+ 330 / 417 79.1%
5%
10%
25%
50%
75%
100% 0-4 3227 / 39230 8.2%
2013 2015
Engagement 9.81 11.374
Giving-Independent Engagement
8.425 10.045
Giving Participation
8.20% 8.42%
2013 2015
Engagement 11.43 12.33
Giving-Independent Engagement
10.266 11.375
Giving Participation
5.58% 5.25%
GeneralPopulation
8.20% 8.42%
2013 2015
Engagement 12.844 15.135
Giving-Independent Engagement
10.031 12.282
Giving Participation
14.46% 13.79%
GeneralPopulation
8.20% 8.42%
2013 2015
Engagement 29.16 36.81
Giving-Independent Engagement
21.41 27.94
Giving Participation
32.7% 29.4%
GeneralPopulation
8.20% 8.42%
2013 2015
Engagement 8.62 10.04
Giving-Independent Engagement
7.77 9.24
Giving Participation
3.86% 4.33%
GeneralPopulation
8.20% 8.42%
2013 2015
Engagement 8.89 10.22
Giving-Independent Engagement
7.96 9.26
Giving Participation
4.34% 5.07%
GeneralPopulation
8.20% 8.42%
2013 2015
Engagement 8.91 8.97
Giving-Independent Engagement
8.04 8.16
Giving Participation
4.17% 4.31%
GeneralPopulation
8.20% 8.42%
Next Steps
What would be do different?